Rapid star locating method for CCD image based on cross projection and differential extremum algorithm

Abstract

Development of the CCD sensor calls for a rapid star image processing speed and the basic work is to extract star points correctly. The typical histogram of a star image has a two-peak value structure and majority of the pixels belong to the background. In this paper, the noise of the image is removed with the adaptive Wiener filter. The background and the star points are distinguished using a window transform on the pixel gray value, all pixels whose gray value below the threshold are set to zero, where the threshold is determined with the optimal iterative algorithm or the Otsu algorithm. We locate the star points with an improved cross projection algorithm. Firstly, preprocess the star image into a binary image with the threshold determined above. Then calculate the horizontal and vertical projections of the binary image. In each projection direction, extract the domain bound of the star points with the proposed binarization differential extremum method which is different from the traditional marker method. Finally, the precise positions of the star points are calculated with the weighted centroid algorithm. Experiments on simulated and real star images show that the improved algorithm has a high processing speed (~300ms) and good centroid locating accuracy.